Since the development of deep learning, several excellent backbones (such as SegNet and U-Net) have been widely applied for image segmentation and can obtain satisfactory results [1]}, [2]}.
The first 13-layer convolutional networks of VGG16 are applied in SegNet as an encoder, and SoftMax is applied for classification after the decoder.
U-Net is an end-to-end net of image segmentation with an encoder and a decoder.
It was designed for medical image segmentation, which meets the similar requirements of biovolume measurement as both of them are microscopic images, and the datasets are limited.
SegNet and U-Net train the yeast dataset for 60 epochs with a batch size of 8.
The CNN-based models can be trained independently without relying on feature engineering, and the models can obtain the expected results, showing strong generalization abilities.
The most-used pre-processing approach in deep learning is data augmentation to avoid overfitting and to increase generalization capabilities, but a limited pre-processing technique is used for denoising [3]}.
Here, SegNet and U-Net are end-to-end models trained without any pre-processing or pre-training.
The segmentation results of SegNet and U-Net are shown in Fig. REF .
The results show that U-Net has better performance for biovolume measurement, which means that concatenated operation of encoder and decoder can improve the precision of segmentation.
<FIGURE>